Staff Data Engineer

RevolutionParts is not just a pioneering force in the automotive eCommerce realm; we're actively seeking passionate and talented individuals to join our squad of Revolutionaries (yes, that's what we call ourselves!). As leaders in providing streamlined, user-friendly solutions, we empower automotive brands to maximize online sales. Our commitment to technology, top-notch customer service, and a profound understanding of the automotive market sets us apart. If you're ready to revolutionize the eCommerce space for automotive parts and accessories, consider joining our dynamic team of Revolutionaries. The Role Most data engineering roles hand you a Jira board. This one hands you a whiteboard and asks what should be on it. RevolutionParts powers parts and accessories commerce for thousands of automotive dealers and OEMs across North America. The data behind all of it (catalog, pricing, inventory) moves through a high-volume ingestion system that has scaled with the business. It was the right architecture for where we were. It isn’t the right architecture for where we’re going. We need someone who can keep this system reliable today while making it obsolete on a timeline they define. The target architecture doesn't exist yet. The technical bar for this domain gets set by whoever takes this role. If that's an uncomfortable amount of open space, this probably isn't the right fit. [... source excerpt omitted ...] s that cross team boundaries, have no clear owner, and have already resisted resolution. Execution & Operational Excellence Hold ultimate accountability for the architecture and production performance of our catalog, pricing, and inventory ingestion systems, with the technical depth to make decisions no one else in the organization is positioned to make. Define the reliability bar for data across the organization. Build the monitoring, alerting, and validation frameworks that turn data quality from a best-effort into a contractual commitment with clear SLAs and owners. Make final, binding technical debt decisions for the ingestion domain, weighing immediate stability against lo [... source excerpt omitted ...] ugh direct mentorship of Senior Engineers on distributed systems, high-volume database performance, and data modeling at scale. Your impact here compounds beyond your own output. Requirements 10+ years in data or software engineering, at least 3 at Staff level or equivalent owning architectural decisions on high-volume production systems. Python and Spark/PySpark at petabyte scale — production systems, not notebooks. You tune Spark from first principles: partition strategy, join optimization, dynamic allocation, skew diagnosis. Designed and operated distributed job execution systems: dynamic compute provisioning, variable workload profiles, job isolation, and resource contention